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Hybrid RESNET and regional convolution neural network for accident estimation (2022)
Journal Article
Djenouri, Y., Srivastava, G., Djenouri, D., Belhadi, A., & Jerry, C. L. (in press). Hybrid RESNET and regional convolution neural network for accident estimation. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/TITS.2022.3165156

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT ex... Read More about Hybrid RESNET and regional convolution neural network for accident estimation.

LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST (2022)
Conference Proceeding
Mustapha, K., Djenouri, D., Khiati, M., Jianguo, D., & Djenouri, Y. (2022). LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN) (694-699). https://doi.org/10.1109/MSN53354.2021.00107

The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MA... Read More about LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST.

Intelligent deep fusion network for anomaly identification in maritime transportation systems (2022)
Journal Article
Djenouri, Y., Belhadi, A., Djenouri, D., Srivastava, G., & Lin, J. C. (in press). Intelligent deep fusion network for anomaly identification in maritime transportation systems. IEEE Transactions on Intelligent Transportation Systems, 1-9. https://doi.org/10.1109/tits.2022.3151490

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime dat... Read More about Intelligent deep fusion network for anomaly identification in maritime transportation systems.

Emergent deep learning for anomaly detection in internet of everything (2021)
Journal Article
Djenouri, Y., Djenouri, D., Belhadi, A., Srivastava, G., & Lin, J. C. W. (in press). Emergent deep learning for anomaly detection in internet of everything. IEEE Internet of Things, https://doi.org/10.1109/JIOT.2021.3134932

This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environment... Read More about Emergent deep learning for anomaly detection in internet of everything.

On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications (2021)
Journal Article
Laidi, R., Djenouri, D., & Balasingham, I. (in press). On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications. IEEE Transactions on Systems Man and Cybernetics: Systems, https://doi.org/10.1109/TSMC.2021.3116141

Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consu... Read More about On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications.

Towards energy efficient clustering in wireless sensor networks: A comprehensive review (2021)
Journal Article
Merabtine, N., Djenouri, D., & Zegour, D. E. (2021). Towards energy efficient clustering in wireless sensor networks: A comprehensive review. IEEE Access, 9, 92688-92705. https://doi.org/10.1109/access.2021.3092509

Clustering is one of the fundamental approaches used to optimize energy consumption in wireless sensor networks. Clustering protocols proposed in the literature can be classified according to different criteria related to their features such as the c... Read More about Towards energy efficient clustering in wireless sensor networks: A comprehensive review.

Towards optimized one-step clustering approach in wireless sensor networks (2021)
Journal Article
Merabtine, N., Djenouri, D., Zegour, D., Bounnssairi, A., & Rahmani, K. (2021). Towards optimized one-step clustering approach in wireless sensor networks. Wireless Personal Communications, 120, 1501–1523. https://doi.org/10.1007/s11277-021-08521-0

This paper introduces a nonlinear integer programming model for the clustering problem in wireless sensor networks, with a threefold contribution. First, all factors that may influence the energy consumption of clustering protocols, such as cluster-h... Read More about Towards optimized one-step clustering approach in wireless sensor networks.

Trajectory outlier detection: New problems and solutions for smart cities (2021)
Journal Article
Djenouri, Y., Djenouri, D., & Chun-Wei Lin, J. (2021). Trajectory outlier detection: New problems and solutions for smart cities. ACM Transactions on Knowledge Discovery from Data, 15(2), https://doi.org/10.1145/3425867

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for th... Read More about Trajectory outlier detection: New problems and solutions for smart cities.

Deep learning vs. traditional solutions for group trajectory outliers (2020)
Journal Article
Belhadi, A., Djenouri, Y., Djenouri, D., Michalak, T., & Chun-Wei Lin, J. (in press). Deep learning vs. traditional solutions for group trajectory outliers. IEEE Transactions on Cybernetics, https://doi.org/10.1109/TCYB.2020.3029338

This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories: 1) algorithms based on data mining and knowledge discovery, whi... Read More about Deep learning vs. traditional solutions for group trajectory outliers.

When the decomposition meets the constraint satisfaction problem (2020)
Journal Article
Djenouri, Y., Djenouri, D., Habbas, Z., Lin, J. C., Michalak, T. P., & Cano, A. (2020). When the decomposition meets the constraint satisfaction problem. IEEE Access, 8, 207034-207043. https://doi.org/10.1109/access.2020.3038228

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is th... Read More about When the decomposition meets the constraint satisfaction problem.

A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories (2020)
Journal Article
Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Cano, A., & Lin, J. C. W. (2021). A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4496-4506. https://doi.org/10.1109/tits.2020.3022612

This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a two phase-based algorithm. The first phase de... Read More about A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories.

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection (2020)
Journal Article
Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J. C., & Fortino, G. (2021). Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection. Information Fusion, 65, 13-20. https://doi.org/10.1016/j.inffus.2020.08.003

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories... Read More about Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection.

A recurrent neural network for urban long-term traffic flow forecasting (2020)
Journal Article
Belhadi, A., Djenouri, Y., Djenouri, D., & Lin, J. C. (2020). A recurrent neural network for urban long-term traffic flow forecasting. Applied Intelligence, 50(10), 3252-3265. https://doi.org/10.1007/s10489-020-01716-1

This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, na... Read More about A recurrent neural network for urban long-term traffic flow forecasting.

DFIOT: Data Fusion for Internet of Things (2020)
Journal Article
Boulkaboul, S., & Djenouri, D. (2020). DFIOT: Data Fusion for Internet of Things. Journal of Network and Systems Management, 28(4), 1136-1160. https://doi.org/10.1007/s10922-020-09519-y

In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data... Read More about DFIOT: Data Fusion for Internet of Things.

IoT-DMCP: An IoT data management and control platform for smart cities (2019)
Conference Proceeding
Boulkaboul, S., Djenouri, D., Bouhafs, S., & Belaid, M. (2019). IoT-DMCP: An IoT data management and control platform for smart cities. https://doi.org/10.5220/0007861005780583

This paper presents a design and implementation of a data management platform to monitor and control smart objects in the Internet of Things (IoT). This is through IPv4/IPv6, and by combining IoT specific features and protocols such as CoAP, HTTP and... Read More about IoT-DMCP: An IoT data management and control platform for smart cities.

A novel parallel framework for metaheuristic-based frequent itemset mining (2019)
Conference Proceeding
Fournier-Viger, P., Bendjoudi, A., Belhadi, A., Djenouri, D., Djenouri, Y., & Lin, J. C. (2019). A novel parallel framework for metaheuristic-based frequent itemset mining. https://doi.org/10.1109/cec.2019.8790116

Frequent Itemset Mining (FIM) is an important but very time-consuming data mining task. As a result, traditional FIM algorithms are often not scalable to large databases. To address this issue, several metaheuristics have been developed in recent yea... Read More about A novel parallel framework for metaheuristic-based frequent itemset mining.

Wireless energy efficient occupancy-monitoring system for smart buildings (2019)
Journal Article
Lasla, N., Doudou, M., Djenouri, D., Ouadjaout, A., & Zizoua, C. (2019). Wireless energy efficient occupancy-monitoring system for smart buildings. Pervasive and Mobile Computing, 59, https://doi.org/10.1016/j.pmcj.2019.101037

© 2019 Elsevier B.V. Rationalizing energy consumption in smart buildings is considered in this paper, and a wireless monitoring system based on Passive Infrared sensors (PIRs) is proposed. The proposed system is pervasive and can be integrated in exi... Read More about Wireless energy efficient occupancy-monitoring system for smart buildings.

Machine learning for smart building applications: Review and taxonomy (2019)
Journal Article
Djenouri, D., Laidi, R., Djenouri, Y., & Balasingham, I. (2019). Machine learning for smart building applications: Review and taxonomy. ACM Computing Surveys, 52(2), 1-36. https://doi.org/10.1145/3311950

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first clas... Read More about Machine learning for smart building applications: Review and taxonomy.

A Survey on Urban Traffic Anomalies Detection Algorithms (2019)
Journal Article
Djenouri, Y., Belhadi, A., Lin, J. C., Djenouri, D., & Cano, A. (2019). A Survey on Urban Traffic Anomalies Detection Algorithms. IEEE Access, 7, 12192-12205. https://doi.org/10.1109/access.2019.2893124

© 2019 IEEE. This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups solutions tha... Read More about A Survey on Urban Traffic Anomalies Detection Algorithms.

Frequent itemset mining in big data with effective single scan algorithms (2018)
Journal Article
Djenouri, Y., Djenouri, D., Chun-Wei Lin, J., & Belhadi, A. (2018). Frequent itemset mining in big data with effective single scan algorithms. IEEE Access, 6, 68013-68026. https://doi.org/10.1109/ACCESS.2018.2880275

© 2013 IEEE. This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel imp... Read More about Frequent itemset mining in big data with effective single scan algorithms.